1. Neuronal avalanches are increasingly recognized to be important for cortex function. My Section took the lead in organizing the first conference on Criticality in Neural Systems in collaboration with Ernst Niebur, Johns Hopkins University. In April 2012, the 2-day conference took place on the NIH campus in Bethesda at the Natcher Conference center with about 100 attendees and featured 19 international and national speakers and posters. Since then, a book with about 22 chapters and international authors, most of who presented at the conference, has been assembled and was published in Spring 2014 (Plenz, D. & Niebur, E. Criticality in Neural Systems, Wiley-VCH, Berlin (2014). The book covers all major aspects of criticality in the brain and is on track to become a standard text book for a rapidly increasing field of critical phenomena in the brain. Besides being the main editor, my Section has contributed 4 chapters covering our major accomplishments demonstrating criticality in the brain from in vitro preparations to the awake animals and normal human subjects. 2. Neuronal avalanches identify critical brain dynamics at which several aspects of information processing are optimized as demonstrated in our previous work. Several classes of critical systems have been identified based on the precise critical exponents that control a systems performance at criticality. One of the main issues though was the proper identification of power laws in critical cortical dynamics. We clarified several misconceptions in the literature regarding the proper identification of cut-offs in scale-invariant critical dynamics. The proper identification of the valid range of power law scaling is a prerequisite for careful evalution of critical brain dynamics (Yu et al. 2014 PLoS One). Abstract: Scale-Invariant Neuronal Avalanche Dynamics and the Cut-off in Size Distributions Identification of cortical dynamics strongly benefits from the simultaneous recording of as many neurons as possible. Yet current technologies provide only incomplete access to the mammalian cortex from which adequate conclusions about dynamics need to be derived. Here, we identify constraints introduced by sub-sampling with a limited number of electrodes, i.e. spatial windowing, for well-characterized critical dynamics &#8213; neuronal avalanches. The local field potential (LFP) was recorded from premotor and prefrontal cortices in two awake macaque monkeys during rest using chronically implanted 96-microelectrode arrays. Negative deflections in the LFP (nLFP) were identified on the full as well as compact sub-regions of the array quantified by the number of electrodes N (10 95), i.e., the window size. Spatiotemporal nLFP clusters organized as neuronal avalanches, i.e., the probability in cluster size, p(s), invariably followed a power law with exponent 1.5 up to N, beyond which p(s) declined more steeply producing a cut-off that varied with N and the LFP filter parameters. Clusters of size s &#8804; N consisted mainly of nLFPs from unique, non-repeated cortical sites, emerged from local propagation between nearby sites, and carried spatial information about cluster organization. In contrast, clusters of size s > N were dominated by repeated site activations and carried little spatial information reflecting greatly distorted sampling conditions. Our findings were confirmed in a neuron-electrode network model. Thus, avalanche analysis needs to be constrained to the size of the observation window to reveal the underlying scale-invariant organization produced by locally unfolding, predominantly feed-forward neuronal cascades. 3. To facilitate the use of neuronal avalanche metrics, we published a software package custom-designed in python that allows for relatively easy power law fits to acquired data (Alstott et al., 2014, PLoS One). Abstract: powerlaw: a Python package for analysis of heavy-tailed distributions Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years selective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible. 4. We filed for a patent that allows quantification of the behaviorally detrimental effects of sleep deprivation using neuronal avalanche based metrics (Plenz et al. 2013). For this project, our group did not perform primary data collection, but performed secondary analysis of clinical data provided by Dr. Giulio Tononi at the University of Wisconsin, USA.